Photosynthetic difference of six poplar genotypes and estimation of photosynthetic capacities based on leaf hyperspectral reflectance

Abstract

Effectively evaluating and estimating the photosynthetic capacities of different poplar genotypes is essential for selecting and breeding poplars with high productivity. This study measured leaf hyperspectral reflectance, net photosynthetic rate (Pn), transpiration rate (Tr), intercellular CO2 concentration (Ci), and stomatal conductance (Gs) across the upper-, middle- and lower-layer leaves of six poplar genotypes. Photosynthetic capacities and spectral differences were assessed among these genotypes. By analyzing the correlation of photosynthetic parameters and spectral characteristics, the photosynthetic parameters were also estimated from hyperspectral parameters using BP neural networks. Significant differences were observed in the photosynthetic parameters among six poplar genotypes. Populus tremula × P. alba exhibited the highest photosynthetic rate, while Populus hopeiensis showed the lowest. Leaves in the middle layer demonstrated greater photosynthetic capacities than those in the other layers. Leaf reflectance among the six poplar genotypes differed significantly in the ranges of 400−760 nm, 800−1,300 nm, 1,500−1,800 nm, and 1,900−2,000 nm. Values for MTCI, WI, REP, PRI, and first-order derivative at 891 nm also showed significant differences. Hyperspectral parameters, including first-order derivative spectra (FDS), raw spectral reflectance, and photosynthetic parameters, showed strong correlations in the red light (670 nm), near-infrared (760−940 nm), and short-wave infrared (1,800−2,500 nm). Four photosynthetic parameters including Pn, Tr, Ci, and Gs were estimated using BP neural network models and R2 were 0.56, 0.44, 0.35, and 0.35, respectively. The present results indicate that hyperspectral reflectance can effectively distinguish between different poplar genotypes and estimate photosynthetic parameters, highlighting its great potential for studying plant phenomics.

Plant phenotyping relevance

葉のハイパースペクトル反射から光合成形質を推定する手法とBPニューラルネットワークモデルを中心に扱っており、植物フェノタイピング手法の開発・適用に該当する。

Code and data availability

The paper's phenotype datasets (leaf hyperspectral reflectance and photosynthetic parameters for six poplar genotypes) and BP neural network analysis code are not publicly deposited; the authors state they are available only on request. No authors' public URL for data or code is provided.

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